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Received β€” 10 September 2026 ⏭

Targeting KRAS reprograms a Treg-dominant immunosuppressive microenvironment and sensitizes KRAS-mutant gastric adenocarcinoma to CTLA-4 immunotherapy

βœ‡(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Xiaohuan Tang Β· Peiyao Yang Β· Xiaoyong Guo Β· Shuhua Wei Β· Xiaojing Cheng Β· Mengyuan Li Β· Yigang Bao Β· Sachiyo Nomura Β· Yu Sun Β· Longtao Huangfu Β· Xiaofang Xing Β· Yongning Jia Β· Fei Shan Β· Xiangyu Gao Β· Chuanhui Han Β· Ziyu Li
9 September 2026 at 18:00

Sci China Life Sci. 2026 Sep 3. doi: 10.1007/s11427-026-3438-4. Online ahead of print.

ABSTRACT

Oncogenic KRAS mutations define a distinct molecular subset of gastric adenocarcinoma (GA), yet their impact on the tumor immune microenvironment remains incompletely understood. In this study, we established a genetically faithful and immunocompetent KRASG12D-driven mouse model of GA, together with matched organoids and cell lines, to investigate how oncogenic KRAS shapes tumor-immune interactions. KRAS-mutant tumors consistently developed an immunosuppressive microenvironment characterized by enrichment of regulatory T cells (Tregs), accompanied by reduced cytotoxic lymphocyte infiltration and intrinsic resistance to PD-1 blockade. Although pharmacologic targeting of KRAS effectively suppressed tumor growth and increased immune cell infiltration, functional immune analyses revealed persistent Treg-mediated immunosuppression that limited effective antitumor immunity. Mechanistically, TGF-Ξ² signaling was required to maintain Treg dominance and suppress effector T cell function in KRAS-driven tumors. Importantly, disruption of this suppressive axis through combined KRAS inhibition and CTLA-4 blockade attenuated TGF-Ξ² activity, impaired Treg function, and enhanced antitumor immune responses in vivo. Collectively, these findings identify oncogenic KRAS as a key regulator of TGF-Ξ²-dependent immune suppression in GA and provide mechanistic insight into immune evasion within this molecular subtype.

PMID:42714795 | DOI:10.1007/s11427-026-3438-4

Received β€” 5 March 2026 ⏭

How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Ziwen Xu Β· Kewei Xu Β· Haoming Xu Β· Haiwen Hong Β· Longtao Huang Β· Hui Xue Β· Ningyu Zhang Β· Yongliang Shen Β· Guozhou Zheng Β· Huajun Chen Β· Shumin Deng
4 March 2026 at 13:00
arXiv:2603.02578v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed in socially sensitive domains, yet their unpredictable behaviors, ranging from misaligned intent to inconsistent personality, pose significant risks. We introduce SteerEval, a hierarchical benchmark for evaluating LLM controllability across three domains: language features, sentiment, and personality. Each domain is structured into three specification levels: L1 (what to express), L2 (how to express), and L3 (how to instantiate), connecting high-level behavioral intent to concrete textual output. Using SteerEval, we systematically evaluate contemporary steering methods, revealing that control often degrades at finer-grained levels. Our benchmark offers a principled and interpretable framework for safe and controllable LLM behavior, serving as a foundation for future research.
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